The background description provided herein is for the purpose of generally presenting the context of the present invention. The subject matter discussed in the background of the invention section should not be assumed to be prior art merely as a result of its mention in the background of the invention section. Similarly, a problem mentioned in the background of the invention section or associated with the subject matter of the background of the invention section should not be assumed to have been previously recognized in the prior art. The subject matter in the background of the invention section merely represents different approaches, which in and of themselves may also be inventions. Work of the presently named inventors, to the extent it is described in the background of the invention section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present invention.
With development of image sensor (cmos) technologies, image collection using high-pixel high-profile cameras has gradually become widespread. In an actual application, a camera with a higher number of pixels indicates better image quality and a stronger capability of parsing an image. However, a required data record volume also becomes much relatively larger. Therefore, requirements on storage devices and bandwidth requirements on signal processing also become much higher. In this case, a great challenge is brought to storage, transmission, and processing of images. Currently, the pixel number of a mainstream cmos sensor have reached 1600 w pixels, and the pixel number of a single-lens reflex camera may be greater. By recording data in a raw format, the raw record of a single image having 1600 w pixels and a bit width of each pixel being 14 bits may reach 224000000 data bits, i.e., 28000000 bytes, and the data volume of a 30-frame per second may be 8400,000,000 bytes. However, a current data transmission rate in a mainstream recording device is below 500 M, which cannot meet the normal storage requirements.
Currently, a commonly used means in the industry adopts the manner of image and video compression by combining software and hardware, such that image storage pressure may be relieved. However, in this manner, color space conversion is first performed on sensor data in the raw format, and then lossy compression and encoding are performed according to visual characteristics and patterns of human eyes, and spatial and time characteristics of the image or video, so as to reduce an actual data storage volume. However, commonly used compression manners all have limitations.
For example, image compression manners, such as jpg and png, have high compression efficiency. However, due to the frequency domain conversion that is used, a loss of low-frequency information is relatively large, leaving less of the reserved information about raw data of the image, which does not facilitate recovery of raw data.
For the jpeg2000 lossless compression manner, the implementation manner is complex, and software operation efficiency is low, which is not suitable for continuous collection of images.
Further, for common video compression encoding (h264/mpeg), multiple compression algorithms are used in a space domain and time domain to implement real-time video image compression. However, a loss of image information is relative large, which is not suitable for recovery of raw data. Moreover, hardware costs are excessively high.
Therefore, a heretofore unaddressed need exists in the art to address the aforementioned deficiencies and inadequacies.